Demand forecasting is the technique of estimating future consumer demand over a certain time period utilising history data and information.

Proper demand forecasting provides organisations with valuable information regarding their prospectives in their current and other markets, allowing managers to make informed pricing, business growth plans, and market potential decisions.

Without demand forecasting, firms risk making bad decisions about their products and target markets – and such decisions can have far-reaching consequences for inventory holding costs, customer satisfaction, supply chain management, and profitability.

Running a business is not a menial task. You never really know how it will all turn out, yet you need to be able to answer questions like these:

  • How many units of inventory do you need to have on hand to be at full stock for each SKU?
  • How often do you project to replenish inventory?
  • How will those projections change over time?
  • Where do you expect to be a year from now?

Maybe you only sort of have an understanding of demand for your products. 

That’s fine! Forecasting projections is one of the toughest things to get right. And even when you’ve been doing it for a while and start to get the hang of it, your projections shift again.

Whether your brand is experiencing gradual sales or is in high-growth mode, we’ll walk you through some tips to improve your ability to forecast demand.What is demand forecasting?

Demand forecasting is the process of using predictive analysis of historical data to estimate and predict customers’ future demand for a product or service. Demand forecasting helps the business make better-informed supply decisions that estimate the total sales and revenue for a future period of time.

Through demand forecasting, businesses can optimise inventory by predicting future sales from analysing historical sales data to make informed business decisions about everything from inventory planning and warehousing needs to running flash sales and meeting customer expectations.

 

 

Most classic demand forecasting methodologies can be divided into three categories:

Qualitative forecasting

When there isn’t a lot of data to work with, such as when a business is new or a product is brought to the market, qualitative forecasting approaches are used. Other information, such as expert opinions, market research, and comparative assessments, is employed in this case to produce quantitative estimations of demand.

This strategy is frequently used in fields such as technology, where new goods may be unique and client demand is difficult to predict ahead of time.

Time series Analysis

When historical data for a product or product line is available and patterns are obvious, organisations typically employ the time series analysis technique to demand forecasting. A time series analysis can help you detect seasonal variations in demand, cyclical patterns, and major sales trends.

The time series analysis approach works best for well-established organisations with several years of data to work with and very steady trend patterns.

Causal models

Because it incorporates detailed information on links between variables affecting market demand, such as rivals, economic pressures, and other socioeconomic factors, the causal model is the most advanced and complex forecasting tool for enterprises. Historical data, like time series analysis, is essential for developing a causal model forecast.

For example, a clothing retail company could develop a causal model forecast by considering factors such as their historical sales data, marketing budget, promotional activities, any new clothing stores in their area, the prices of their competitors, the weather, overall demand in their area, and even their local unemployment rate.

Benefits of demand forecasting

Without demand, there is no business. And without a thorough understanding of demand, businesses aren’t capable of making the right decisions about marketing spend, production, staffing, and more.

Demand forecasting will never be 100% accurate, but there are steps you can take to improve production lead times, increase operational efficiencies, save money, launch new products, and provide a better customer experience.

Preparing your budget

Demand forecasting helps reduce risks and make efficient financial decisions that impact profit margins, cash flow, allocation of resources, opportunities for expansion, inventory accounting, operating costs, staffing, and overall spend. All strategic and operational plans are formulated around forecasting demand.

Planning and scheduling production

Demand forecasting lets you provide the products your customers want, when they want them. Forecasting demand requires that order fulfilment is synced up with your marketing prior to launching.

Nothing kills progress (or your reputation) faster than being sold out for weeks on end. Proper demand forecasting and inventory control can help ensure a business doesn’t buy insufficient or excessive inventory.

 

 

Storing inventory

Demand forecasting can help you spend less money on both inventory purchase orders and warehousing as the more inventory you carry, the more expensive it is to store. Good inventory management involves having enough product on hand but not too much.

Closely tracking inventory levels lets you easily restock and forecast inventory over time.

 

Demand forecasting mistakes in the retail industry

 

Developing a pricing strategy

Demand forecasting isn’t just about perfecting a business’s production schedule to supply demand, but it should also help price products based on the demand. Understanding the market and potential opportunities, businesses can grow, formulate competitive pricing, employ the right marketing strategies, and invest in their growth.

If you choose to slash prices or put an item on promotion, demand may temporarily increase for that product. Without that sale, you may not have experienced the boost.

If there is limited supply of a high-demand product, you can use the scarcity principle to increase the price as an exclusive offer. You must keep an eye on new entrants though as supply may increase.

 

While integrating machine learning-based demand forecasting provides a strong basis for getting started with applied AI, your company’s journey should not end there. A wide range of common planning difficulties in retail have previously been successfully addressed by AI, from staff optimization to improved in-store inventory management to more automated and effective markdown optimization. By implementing practical AI throughout all of retail’s basic processes, there is a wealth of surprisingly simple, immediate wins to be had.

Demand forecasting enables firms to make more informed decisions about anything from managing inventory to supply chain efficiency. With customer expectations changing faster than ever, businesses need a method to accurately forecast demand.

 

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Author

  • Technical Writer at Keepler. "I've been a technical writer and instructional designer for different industries for a decade now and I still haven't stopped learning. When I'm not reading and writing about new methodologies you can find me writing science fiction."